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Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery

Granados, A; Lucena, O; Vakharia, V; Miserocchi, A; McEvoy, AW; Vos, SB; Rodionov, R; ... Ourselin, S; + view all (2020) Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery. In: Proceedings of the 17th International Symposium on Biomedical Imaging (ISBI) IEEE 2020. (pp. pp. 674-677). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

Implantation accuracy of electrodes during stereotactic neurosurgery is necessary to ensure safety and efficacy. However, electrodes deflect from planned trajectories. Although mechanical models and data-driven approaches have been proposed for trajectory prediction, they lack to report uncertainty of the predictions. We propose to use Monte Carlo (MC) dropout on neural networks to quantify uncertainty of predicted electrode local displacement. We compute image features of 23 stereoelectroencephalography cases (241 electrodes) and use them as inputs to a neural network to regress electrode local displacement. We use MC dropout with 200 stochastic passes to quantify uncertainty of predictions. To validate our approach, we define a baseline model without dropout and compare it to a stochastic model using 10-fold cross-validation. Given a starting planned trajectory, we predicted electrode bending using inferred local displacement at the tip via simulation. We found MC dropout performed better than a non-stochastic baseline model and provided confidence intervals along the predicted trajectory of electrodes. We believe this approach facilitates better decision making for electrode bending prediction in surgical planning.

Type: Proceedings paper
Title: Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery
Event: 17th International Symposium on Biomedical Imaging (ISBI)
ISBN-13: 978-1-5386-9330-8
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ISBI45749.2020.9098730
Publisher version: https://doi.org/10.1109/ISBI45749.2020.9098730
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: stereotactic neurosurgery, epilepsy, trajectory prediction, neural network, uncertainty quantification
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10102634
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